Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?

• URL: http://arxiv.org/abs/2407.01992v1
• Date: Tue, 2 Jul 2024 07:06:53 GMT
• Title: Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?
• Authors: Nishant Balepur, Rachel Rudinger,
• Abstract summary: Recent work shows that large language models (LLMs) can answer multiple-choice questions using only the choices. We use a contrast set that probes if LLMs over-rely on choices-only shortcuts in MCQA. After validating our contrast set, we test 12 LLMs, finding that these models do not exhibit reliance on choice-only shortcuts when given both the question and choices.
• Score: 16.384333600053342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work shows that large language models (LLMs) can answer multiple-choice questions using only the choices, but does this mean that MCQA leaderboard rankings of LLMs are largely influenced by abilities in choices-only settings? To answer this, we use a contrast set that probes if LLMs over-rely on choices-only shortcuts in MCQA. While previous works build contrast sets via expensive human annotations or model-generated data which can be biased, we employ graph mining to extract contrast sets from existing MCQA datasets. We use our method on UnifiedQA, a group of six commonsense reasoning datasets with high choices-only accuracy, to build an 820-question contrast set. After validating our contrast set, we test 12 LLMs, finding that these models do not exhibit reliance on choice-only shortcuts when given both the question and choices. Thus, despite the susceptibility~of MCQA to high choices-only accuracy, we argue that LLMs are not obtaining high ranks on MCQA leaderboards just due to their ability to exploit choices-only shortcuts.

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